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README.md
In surveyexplorer: Quickly Explore Complex Survey Data
Surveyexplorer
Visualize and tabulate single-choice, multiple-choice, matrix-style
questions from survey data. Includes ability to group cross-tabulations,
frequency distributions, and plots by categorical variables and to
integrate survey weights. Ideal for quickly uncovering descriptive
patterns in survey data.
Installation
install.packages("surveyexplorer")
# or devtools::install_github("liamhaller/surveyexplorer") for the devlopment version
Examples
library(surveyexplorer)
The data used in the following examples is from the berlinbears
dataset, a fictional survey of bears in Berlin, that is included in the
surveyexplorer package.
Single-choice questions
#Basic table
single_table(berlinbears,
question = income)
Question: income
n
freq
<1000
82
0.164
1000-2000
50
0.100
2000-3000
177
0.354
3000-4000
109
0.218
5000+
57
0.114
No answer
22
0.044
NA
3
0.006
Column Total
500
—
Use group_by = to partition the question into several groups
single_table(berlinbears,
question = income,
group_by = gender)
Question: income
grouped by: gender
female
male
NA
Rowwise Total
Frequency
Count
Frequency
Count
Frequency
Count
Frequency
Count
<1000
16.74%
39
15.73%
39
21.05%
4
16.40%
82
1000-2000
9.87%
23
9.68%
24
15.79%
3
10.00%
50
2000-3000
35.62%
83
35.89%
89
26.32%
5
35.40%
177
3000-4000
21.89%
51
22.18%
55
15.79%
3
21.80%
109
5000+
11.59%
27
10.89%
27
15.79%
3
11.40%
57
No answer
3.86%
9
4.84%
12
5.26%
1
4.40%
22
NA
0.43%
1
0.81%
2
0.00%
0
0.60%
3
Columnwise Total
46.60%
233
49.60%
248
3.80%
19
100.00%
500
Ignore unwanted subgroups with subgroups_to_exclude
single_table(berlinbears,
question = income,
group_by = gender,
subgroups_to_exclude = NA)
Question: income
grouped by: gender
female
male
Rowwise Total
Frequency
Count
Frequency
Count
Frequency
Count
<1000
16.74%
39
15.73%
39
16.22%
78
1000-2000
9.87%
23
9.68%
24
9.77%
47
2000-3000
35.62%
83
35.89%
89
35.76%
172
3000-4000
21.89%
51
22.18%
55
22.04%
106
5000+
11.59%
27
10.89%
27
11.23%
54
No answer
3.86%
9
4.84%
12
4.37%
21
NA
0.43%
1
0.81%
2
0.62%
3
Columnwise Total
48.44%
233
51.56%
248
100.00%
481
Remove NAs from the question variable with na.rm
single_table(berlinbears,
question = income,
group_by = gender,
subgroups_to_exclude = NA,
na.rm = TRUE)
Question: income
grouped by: gender
female
male
Rowwise Total
Frequency
Count
Frequency
Count
Frequency
Count
<1000
16.81%
39
15.85%
39
16.32%
78
1000-2000
9.91%
23
9.76%
24
9.83%
47
2000-3000
35.78%
83
36.18%
89
35.98%
172
3000-4000
21.98%
51
22.36%
55
22.18%
106
5000+
11.64%
27
10.98%
27
11.30%
54
No answer
3.88%
9
4.88%
12
4.39%
21
Columnwise Total
48.54%
232
51.46%
246
100.00%
478
Finally, you can specify survey weights using the weight option
single_table(berlinbears,
question = income,
group_by = gender,
subgroups_to_exclude = NA,
na.rm = TRUE,
weights = weights)
Question: income
grouped by: gender
female
male
Rowwise Total
Frequency
Count
Frequency
Count
Frequency
Count
<1000
15.96%
59.6
17.21%
75.2
16.63%
134.8
1000-2000
10.46%
39.1
10.19%
44.5
10.31%
83.6
2000-3000
33.79%
126.3
33.88%
148.0
33.84%
274.3
3000-4000
25.08%
93.7
25.34%
110.7
25.22%
204.4
5000+
9.82%
36.7
8.68%
37.9
9.21%
74.6
No answer
4.90%
18.3
4.70%
20.5
4.79%
38.8
Columnwise Total
46.10%
373.6
53.90%
436.9
100.00%
810.5
Frequencies and counts are weighted
The same syntax can be applied to the single_freq function to plot
frequencies of the question optionally partitioned by subgroups.
single_freq(berlinbears,
question = income,
group_by = gender,
subgroups_to_exclude = NA,
na.rm = TRUE,
weights = weights)
Multiple-choice questions
The options and syntax for multiple-choice tables multi_table and
graphs multi_graphs are the same. The only difference is the question
input also accommodates tidyselect syntax to select several columns for
each answer option. For example, the question “will_eat” has five answer
options each prefixed by “will_eat”
berlinbears |>
dplyr::select(starts_with('will_eat')) |>
head()
#> will_eat.SQ001 will_eat.SQ002 will_eat.SQ003 will_eat.SQ004 will_eat.SQ005
#> 1 0 1 0 1 1
#> 2 0 1 1 1 1
#> 3 1 1 0 1 1
#> 4 0 0 0 1 0
#> 5 0 0 0 1 1
#> 6 0 0 0 1 0
The same syntax can be used to select the question for the multiple
choice tables and graphs
multi_table(berlinbears,
question = dplyr::starts_with('will_eat'),
group_by = genus,
subgroups_to_exclude = NA,
na.rm = TRUE)
Question: dplyr::starts_with("will_eat")
grouped by: genus
Ailuropoda
Ursus
Rowwise Total
Frequency
Count
Frequency
Count
Frequency
Count
will_eat.SQ004
97.54%
278
91.62%
175
40.12%
453
will_eat.SQ002
59.30%
169
66.49%
127
26.22%
296
will_eat.SQ005
43.86%
125
46.60%
89
18.95%
214
will_eat.SQ001
24.91%
71
27.23%
52
10.89%
123
will_eat.SQ003
8.42%
24
9.95%
19
3.81%
43
Columnwise Total
59.08%
667
40.92%
462
100.00%
1129
For graphing, the multi_freq function creates an UpSet plot to
visualize the frequencies of the intersecting sets for each answer
combination and also includes the ability to specify weights.
multi_freq(berlinbears,
question = dplyr::starts_with('will_eat'),
na.rm = TRUE,
weights = weights)
#> Estimes are only preciese to one significant digit, weights may have been rounded
The graphs can also be grouped
multi_freq(berlinbears,
question = dplyr::starts_with('will_eat'),
group_by = genus,
subgroups_to_exclude = NA,
na.rm = FALSE,
weights = weights)
#> Estimes are only preciese to one significant digit, weights may have been rounded
Matrix Questions
matrix_table has the same syntax as above and works with array or
categorical questions
matrix_table(berlinbears,
dplyr::starts_with('c_'),
group_by = is_parent)
Question: dplyr::starts_with("c_")
grouped by: is_parent
high
low
medium
NA
0
c_diet
6.02% (20)
71.99% (239)
16.57% (55)
5.42% (18)
c_exercise
25% (83)
27.71% (92)
24.1% (80)
23.19% (77)
1
c_diet
3.57% (6)
75% (126)
17.26% (29)
4.17% (7)
c_exercise
19.05% (32)
27.38% (46)
23.81% (40)
29.76% (50)
matrix_freq visualizes the frequencies of responses
matrix_freq(berlinbears,
dplyr::starts_with('p_'),
na.rm = TRUE)
For array/matrix style questions that are numeric matrix_mean plots
the mean values and confidence intervals
matrix_mean(berlinbears,
question = dplyr::starts_with('p_'),
na.rm = TRUE)
#Can also apply grouping + survey weights
matrix_mean(berlinbears,
question = dplyr::starts_with('p_'),
na.rm = TRUE,
group_by = species,
subgroups_to_exclude = NA)
Finally, for Likert questions (scales of 3,5,7,9…) matrix_likert
provides a custom plot
#you can specify custom labels with the `label` argument
matrix_likert(berlinbears,
question = dplyr::starts_with('p_'),
labels = c('Strongly disagree', 'Disagree','Neutral','Agree','Strongly agree'))
#can also apply pass custom colors and specify weights weights
matrix_likert(berlinbears,
question = dplyr::starts_with('p_'),
labels = c('Strongly disagree', 'Disagree','Neutral','Agree','Strongly agree'),
colors = c("#E1AA28", "#1E5F46", "#7E8F75", "#EFCD83", "#E17832"),
weights = weights)
Overview
Functions
- Single-choice
single_tablesingle_freq- Multiple-choice
multi_tablemulti_freq- Matrix
matrix_tablematrix_freqmatrix_meanmatrix_likert
*_table functions return a gt table of the cross tabulations and
frequencies for each question while *_freq returns the same data but
as a plot.
For matrix-style questions with numerical input, matrix_mean plots the
mean value value and ± two standard deviations. matrix_likert
visualizes questions that accept Likert responses (strongly
agree-strongly disagree) or questions with 3,5,7,9… categories.
Syntax
Each function contains the following options
- dataset —The input dataframe (or tibble) of survey questions
- question — The column(s) that contain the response options for a
question, can be selected by using tidyselect semantics or providing a
vector of column names or numbers
- group_by — Optional variable to group the analysis. If provided, the
frequencies and counts will be calculated within each subgroup
- subgroups_to_exclude — Optional vector specifying subgroups to exclude
from the analysis
- weights — Optional variable containing survey weights. If provided,
frequencies and counts will be weighted accordingly
- na.rm — Logical indicating whether to remove NA values from question
before analysis
Try the surveyexplorer package in your browser
Any scripts or data that you put into this service are public.
surveyexplorer documentation built on June 22, 2024, 9:35 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Surveyexplorer
Visualize and tabulate single-choice, multiple-choice, matrix-style questions from survey data. Includes ability to group cross-tabulations, frequency distributions, and plots by categorical variables and to integrate survey weights. Ideal for quickly uncovering descriptive patterns in survey data.
Installation
install.packages("surveyexplorer")
# or devtools::install_github("liamhaller/surveyexplorer") for the devlopment version
Examples
library(surveyexplorer)
The data used in the following examples is from the berlinbears
dataset, a fictional survey of bears in Berlin, that is included in the
surveyexplorer package.
Single-choice questions
#Basic table
single_table(berlinbears,
question = income)
Question: income
n
freq
<1000
82
0.164
1000-2000
50
0.100
2000-3000
177
0.354
3000-4000
109
0.218
5000+
57
0.114
No answer
22
0.044
NA
3
0.006
Column Total
500
—
Use group_by = to partition the question into several groups
single_table(berlinbears,
question = income,
group_by = gender)
Question: income
grouped by: gender
female
male
NA
Rowwise Total
Frequency
Count
Frequency
Count
Frequency
Count
Frequency
Count
<1000
16.74%
39
15.73%
39
21.05%
4
16.40%
82
1000-2000
9.87%
23
9.68%
24
15.79%
3
10.00%
50
2000-3000
35.62%
83
35.89%
89
26.32%
5
35.40%
177
3000-4000
21.89%
51
22.18%
55
15.79%
3
21.80%
109
5000+
11.59%
27
10.89%
27
15.79%
3
11.40%
57
No answer
3.86%
9
4.84%
12
5.26%
1
4.40%
22
NA
0.43%
1
0.81%
2
0.00%
0
0.60%
3
Columnwise Total
46.60%
233
49.60%
248
3.80%
19
100.00%
500
Ignore unwanted subgroups with subgroups_to_exclude
single_table(berlinbears,
question = income,
group_by = gender,
subgroups_to_exclude = NA)
Question: income
grouped by: gender
female
male
Rowwise Total
Frequency
Count
Frequency
Count
Frequency
Count
<1000
16.74%
39
15.73%
39
16.22%
78
1000-2000
9.87%
23
9.68%
24
9.77%
47
2000-3000
35.62%
83
35.89%
89
35.76%
172
3000-4000
21.89%
51
22.18%
55
22.04%
106
5000+
11.59%
27
10.89%
27
11.23%
54
No answer
3.86%
9
4.84%
12
4.37%
21
NA
0.43%
1
0.81%
2
0.62%
3
Columnwise Total
48.44%
233
51.56%
248
100.00%
481
Remove NAs from the question variable with na.rm
single_table(berlinbears,
question = income,
group_by = gender,
subgroups_to_exclude = NA,
na.rm = TRUE)
Question: income
grouped by: gender
female
male
Rowwise Total
Frequency
Count
Frequency
Count
Frequency
Count
<1000
16.81%
39
15.85%
39
16.32%
78
1000-2000
9.91%
23
9.76%
24
9.83%
47
2000-3000
35.78%
83
36.18%
89
35.98%
172
3000-4000
21.98%
51
22.36%
55
22.18%
106
5000+
11.64%
27
10.98%
27
11.30%
54
No answer
3.88%
9
4.88%
12
4.39%
21
Columnwise Total
48.54%
232
51.46%
246
100.00%
478
Finally, you can specify survey weights using the weight option
single_table(berlinbears,
question = income,
group_by = gender,
subgroups_to_exclude = NA,
na.rm = TRUE,
weights = weights)
Question: income
grouped by: gender
female
male
Rowwise Total
Frequency
Count
Frequency
Count
Frequency
Count
<1000
15.96%
59.6
17.21%
75.2
16.63%
134.8
1000-2000
10.46%
39.1
10.19%
44.5
10.31%
83.6
2000-3000
33.79%
126.3
33.88%
148.0
33.84%
274.3
3000-4000
25.08%
93.7
25.34%
110.7
25.22%
204.4
5000+
9.82%
36.7
8.68%
37.9
9.21%
74.6
No answer
4.90%
18.3
4.70%
20.5
4.79%
38.8
Columnwise Total
46.10%
373.6
53.90%
436.9
100.00%
810.5
Frequencies and counts are weighted
The same syntax can be applied to the single_freq function to plot
frequencies of the question optionally partitioned by subgroups.
single_freq(berlinbears,
question = income,
group_by = gender,
subgroups_to_exclude = NA,
na.rm = TRUE,
weights = weights)
Multiple-choice questions
The options and syntax for multiple-choice tables multi_table and
graphs multi_graphs are the same. The only difference is the question
input also accommodates tidyselect syntax to select several columns for
each answer option. For example, the question “will_eat” has five answer
options each prefixed by “will_eat”
berlinbears |>
dplyr::select(starts_with('will_eat')) |>
head()
#> will_eat.SQ001 will_eat.SQ002 will_eat.SQ003 will_eat.SQ004 will_eat.SQ005
#> 1 0 1 0 1 1
#> 2 0 1 1 1 1
#> 3 1 1 0 1 1
#> 4 0 0 0 1 0
#> 5 0 0 0 1 1
#> 6 0 0 0 1 0
The same syntax can be used to select the question for the multiple choice tables and graphs
multi_table(berlinbears,
question = dplyr::starts_with('will_eat'),
group_by = genus,
subgroups_to_exclude = NA,
na.rm = TRUE)
Question: dplyr::starts_with("will_eat")
grouped by: genus
Ailuropoda
Ursus
Rowwise Total
Frequency
Count
Frequency
Count
Frequency
Count
will_eat.SQ004
97.54%
278
91.62%
175
40.12%
453
will_eat.SQ002
59.30%
169
66.49%
127
26.22%
296
will_eat.SQ005
43.86%
125
46.60%
89
18.95%
214
will_eat.SQ001
24.91%
71
27.23%
52
10.89%
123
will_eat.SQ003
8.42%
24
9.95%
19
3.81%
43
Columnwise Total
59.08%
667
40.92%
462
100.00%
1129
For graphing, the multi_freq function creates an UpSet plot to
visualize the frequencies of the intersecting sets for each answer
combination and also includes the ability to specify weights.
multi_freq(berlinbears,
question = dplyr::starts_with('will_eat'),
na.rm = TRUE,
weights = weights)
#> Estimes are only preciese to one significant digit, weights may have been rounded
The graphs can also be grouped
multi_freq(berlinbears,
question = dplyr::starts_with('will_eat'),
group_by = genus,
subgroups_to_exclude = NA,
na.rm = FALSE,
weights = weights)
#> Estimes are only preciese to one significant digit, weights may have been rounded
Matrix Questions
matrix_table has the same syntax as above and works with array or
categorical questions
matrix_table(berlinbears,
dplyr::starts_with('c_'),
group_by = is_parent)
Question: dplyr::starts_with("c_")
grouped by: is_parent
high
low
medium
NA
0
c_diet
6.02% (20)
71.99% (239)
16.57% (55)
5.42% (18)
c_exercise
25% (83)
27.71% (92)
24.1% (80)
23.19% (77)
1
c_diet
3.57% (6)
75% (126)
17.26% (29)
4.17% (7)
c_exercise
19.05% (32)
27.38% (46)
23.81% (40)
29.76% (50)
matrix_freq visualizes the frequencies of responses
matrix_freq(berlinbears,
dplyr::starts_with('p_'),
na.rm = TRUE)
For array/matrix style questions that are numeric matrix_mean plots
the mean values and confidence intervals
matrix_mean(berlinbears,
question = dplyr::starts_with('p_'),
na.rm = TRUE)
#Can also apply grouping + survey weights
matrix_mean(berlinbears,
question = dplyr::starts_with('p_'),
na.rm = TRUE,
group_by = species,
subgroups_to_exclude = NA)
Finally, for Likert questions (scales of 3,5,7,9…) matrix_likert
provides a custom plot
#you can specify custom labels with the `label` argument
matrix_likert(berlinbears,
question = dplyr::starts_with('p_'),
labels = c('Strongly disagree', 'Disagree','Neutral','Agree','Strongly agree'))
#can also apply pass custom colors and specify weights weights
matrix_likert(berlinbears,
question = dplyr::starts_with('p_'),
labels = c('Strongly disagree', 'Disagree','Neutral','Agree','Strongly agree'),
colors = c("#E1AA28", "#1E5F46", "#7E8F75", "#EFCD83", "#E17832"),
weights = weights)
Overview
Functions
- Single-choice
single_tablesingle_freq- Multiple-choice
multi_tablemulti_freq- Matrix
matrix_tablematrix_freqmatrix_meanmatrix_likert
*_table functions return a gt table of the cross tabulations and frequencies for each question while *_freq returns the same data but as a plot.
For matrix-style questions with numerical input, matrix_mean plots the
mean value value and ± two standard deviations. matrix_likert
visualizes questions that accept Likert responses (strongly
agree-strongly disagree) or questions with 3,5,7,9… categories.
Syntax
Each function contains the following options
- dataset —The input dataframe (or tibble) of survey questions
- question — The column(s) that contain the response options for a question, can be selected by using tidyselect semantics or providing a vector of column names or numbers
- group_by — Optional variable to group the analysis. If provided, the frequencies and counts will be calculated within each subgroup
- subgroups_to_exclude — Optional vector specifying subgroups to exclude from the analysis
- weights — Optional variable containing survey weights. If provided, frequencies and counts will be weighted accordingly
- na.rm — Logical indicating whether to remove NA values from question before analysis
Try the surveyexplorer package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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